Import required libraries¶
In [1]:
import cv2
import glob
import numpy as np
from math import *
import matplotlib.pyplot as plt
import matplotlib.image as mpimage
from moviepy.editor import VideoFileClip
from IPython.display import HTML
import moviepy as mve
import matplotlib.gridspec as gridspec
import matplotlib.colors as colors
from ipywidgets import *
from __future__ import print_function
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
from IPython.display import display
%matplotlib inline
Camera Calibration¶
In [2]:
def calibrate_camera(cal_images, nx, ny):
imge=[]
orig=[]
objpoints=[] # 3D points
imgpoints=[] # 2D points
objp=np.zeros((nx*ny,3), np.float32)
objp[:,:2]=np.mgrid[0:nx,0:ny].T.reshape(-1, 2)
fname=cal_images[0]
for fname in cal_images:
img=cv2.imread(fname)
gray=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, corners=cv2.findChessboardCorners(gray, (nx, ny), None)
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
imge.append(cv2.drawChessboardCorners(img, (9,6), corners, ret))
ret, mtx, dist, rvecs, tvecs=cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
return mtx, dist,imge
In [3]:
def camera_setup():
cal_images=glob.glob('camera_cal/calibration*.jpg')
nx, ny=9, 6
cam_mtx, cam_dist,imge= calibrate_camera(cal_images, nx, ny)
return cam_mtx, cam_dist,imge
In [4]:
cam_mtx, cam_dist,imge= camera_setup()
undis_images=[]
for i in imge: undis_images.append(cv2.undistort(i, cam_mtx, cam_dist, None, cam_mtx))
In [5]:
def ShowGrid(X,Y):
h=len(X)
w=2
features_example=[]
plt.figure(figsize=(20,h*6))
gs1=gridspec.GridSpec(h,w)
gs1.update(wspace=0.002, hspace=0.2)
count=0
for i in range(h):
ax1=plt.subplot(gs1[count])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
count+=1
plt.imshow(X[i])
ax1=plt.subplot(gs1[count])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.imshow(Y[i])
count+=1
plt.show()
ShowGrid(imge,undis_images)
Undistort test images¶
In [6]:
cal_images=glob.glob('test_images/test*.jpg')
normal_images=[]
undis_images=[]
for i in cal_images:
img=cv2.imread(i)
img=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
normal_images.append(img)
undis_images.append(cv2.undistort(img, cam_mtx, cam_dist, None, cam_mtx))
ShowGrid(normal_images,undis_images)
Color and gradient threshold¶
In [7]:
def hls_color_thresh(img, thresh_Low, tresh_High):
imgHSV=cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
binary=np.zeros((img.shape[0], img.shape[1]))
binary[(imgHSV[:,:,0] >= thresh_Low[0]) & (imgHSV[:,:,0] <= tresh_High[0]) &
(imgHSV[:,:,1] >= thresh_Low[1]) & (imgHSV[:,:,1] <= tresh_High[1]) &
(imgHSV[:,:,2] >= thresh_Low[2]) & (imgHSV[:,:,2] <= tresh_High[2])]=1
return binary
def sobel_x(img, sobel_kernel=3,min_thres=20, max_thres =100):
imghsl=cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
sobelx1=cv2.Sobel(imghsl[:,:,1], cv2.CV_64F, 1,0, ksize=sobel_kernel)
sobelx2=cv2.Sobel(imghsl[:,:,2], cv2.CV_64F, 1,0, ksize=sobel_kernel)
scl_sobelx1=np.uint8(255*sobelx1/ np.max(sobelx1))
scl_sobelx2=np.uint8(255*sobelx2/ np.max(sobelx2))
binaryx1=np.zeros_like(scl_sobelx1)
binaryx1[(scl_sobelx1 >= min_thres) & (scl_sobelx1 <= max_thres)]=1
binaryx2=np.zeros_like(scl_sobelx2)
binaryx2[(scl_sobelx2 >= min_thres) & (scl_sobelx2 <= max_thres)]=1
binary=np.zeros_like(scl_sobelx1)
binary[(binaryx1 ==1) | (binaryx2 ==1)]=1
return binary
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
gray=cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
sobelx=cv2.Sobel(gray, cv2.CV_64F, 1,0, ksize=sobel_kernel)
sobely=cv2.Sobel(gray, cv2.CV_64F, 0,1, ksize=sobel_kernel)
gradmag=np.sqrt(sobelx**2+sobely**2)
scl_sobel=np.uint8(255*gradmag / np.max(gradmag))
binary=np.zeros_like(scl_sobel)
binary[(scl_sobel >= mag_thresh[0]) & (scl_sobel <= mag_thresh[1])]=1
return binary
def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
gray=cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
sobelx=cv2.Sobel(gray, cv2.CV_64F, 1,0, ksize=sobel_kernel)
sobely=cv2.Sobel(gray, cv2.CV_64F, 0,1, ksize=sobel_kernel)
abs_sobelx=np.absolute(sobelx)
abs_sobely=np.absolute(sobely)
absgraddir=np.arctan2(abs_sobely, abs_sobelx)
binary=np.zeros_like(absgraddir)
binary[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])]=1
return binary
def mag_dir_thresh(img, sobel_kernel=3, mag_thresh=(0, 255), dir_thresh=(0,np.pi/2)):
gray=cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
sobelx=cv2.Sobel(img, cv2.CV_64F, 1,0, ksize=sobel_kernel)
sobely=cv2.Sobel(img, cv2.CV_64F, 0,1, ksize=sobel_kernel)
gradmag=np.sqrt(sobelx**2+sobely**2)
abs_sobelx=np.absolute(sobelx)
abs_sobely=np.absolute(sobely)
absgraddir=np.arctan2(abs_sobely, abs_sobelx)
scl_sobel=np.uint8(255*gradmag / np.max(gradmag))
binary=np.zeros_like(scl_sobel)
binary[(scl_sobel >= mag_thresh[0]) & (scl_sobel <= mag_thresh[1]) & (absgraddir >= dir_thresh[0]) & (absgraddir <= dir_thresh[1]) ]=1
return binary
In [8]:
yellow_low=np.array([0,100,100])
yellow_high=np.array([50,255,255])
white_low=np.array([18,0,180])
white_high=np.array([255,80,255])
def test_Color_Thres(white_low):
white_low=hex_to_rgb(white_low)
print(white_low)
yellow_low=np.array([0,100,100])
yellow_high=np.array([50,255,255])
white_high=np.array([255,80,255])
img=cv2.imread("test_images/test2.jpg")
imgRGB=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
imgThres_yellow=hls_color_thresh(imgRGB,yellow_low,yellow_high)
imgThres_white=hls_color_thresh(imgRGB,white_low,white_high)
imgThres_both =np.zeros_like(imgThres_yellow)
imgThres_both[(imgThres_yellow==1) | (imgThres_white==1)] =1
plt.figure(figsize=(20,10))
gs1=gridspec.GridSpec(2,2)
gs1.update(wspace=0.02, hspace=0.02)
ax1=plt.subplot(gs1[0])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.imshow(imgThres_yellow,cmap ='gray')
ax1=plt.subplot(gs1[1])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.imshow(imgThres_white,cmap ='gray')
ax1=plt.subplot(gs1[2])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.imshow(imgThres_both,cmap ='gray')
ax1=plt.subplot(gs1[3])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.imshow(imgRGB)
In [9]:
def hex_to_rgb(hex_string):
rgb=colors.hex2color(hex_string)
return np.array(tuple([int(255*x) for x in rgb]))
Widget to easily select best color¶
In [10]:
w_low=widgets.ColorPicker(
description='White Low',concise=False,value='#1200b4'
)
interact(test_Color_Thres,white_low=w_low)
Out[10]:
In [11]:
img=cv2.imread("test_images/test1.jpg")
imgRGB=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
magThr =mag_thresh(imgRGB, 3, (50, 100))
dirThr =dir_threshold(imgRGB, 9,(np.pi/240/90, np.pi/2*60/90))
imgThr=sobel_x(imgRGB,9,80,220)
plt.figure(figsize=(30,20))
gs1=gridspec.GridSpec(2,2)
gs1.update(wspace=0.02, hspace=0.02)
ax1=plt.subplot(gs1[0])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.title('Original Image')
plt.imshow(imgRGB)
ax1=plt.subplot(gs1[1])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.title('Magnitude threshold Image')
plt.imshow(magThr,cmap='gray')
ax1=plt.subplot(gs1[2])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.title('Direction threshold Image')
plt.imshow(dirThr,cmap='gray')
ax1=plt.subplot(gs1[3])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.title('Sobel x only threshold Image')
plt.imshow(imgThr,cmap='gray')
Out[11]:
Perspective Transformation¶
In [12]:
img_size=(imgThr.shape[1], imgThr.shape[0])
src = np.float32(
[[(img_size[0] / 2) - 55, img_size[1] / 2 + 100],
[((img_size[0] / 6) - 10), img_size[1]],
[(img_size[0] * 5 / 6) + 60, img_size[1]],
[(img_size[0] / 2 + 55), img_size[1] / 2 + 100]])
dst = np.float32(
[[(img_size[0] / 4), 0],
[(img_size[0] / 4), img_size[1]],
[(img_size[0] * 3 / 4), img_size[1]],
[(img_size[0] * 3 / 4), 0]])
src=np.rint(src)
dst=np.rint(dst)
M_persp=cv2.getPerspectiveTransform(src, dst)
Minv_persp=cv2.getPerspectiveTransform(dst, src)
img_line=cv2.line(imgRGB,(src[0,0],src[0,1]),(src[1,0],src[1,1]),(255,0,0),2)
img_line=cv2.line(img_line,(src[2,0],src[2,1]),(src[3,0],src[3,1]),(255,0,0),2)
perspective=cv2.warpPerspective(img_line, M_persp, img_size, flags=cv2.INTER_LINEAR)
plt.figure(figsize=(30,20))
gs1=gridspec.GridSpec(1,2)
gs1.update(wspace=0.02, hspace=0.02)
ax1=plt.subplot(gs1[0])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.title('Original image')
plt.imshow(img_line, cmap='gray')
ax1=plt.subplot(gs1[1])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.title('Perspective')
plt.imshow(perspective, cmap='gray')
Out[12]:
In [13]:
img=cv2.imread("test_images/test4.jpg")
imgRGB=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img2=cv2.undistort(imgRGB, cam_mtx, cam_dist, None, cam_mtx)
img_size=img2.shape[1::-1]
RGB_warped=cv2.warpPerspective(img2, M_persp, img_size, flags=cv2.INTER_LINEAR)
img_unpersp=cv2.warpPerspective(RGB_warped, Minv_persp, img_size, flags=cv2.INTER_LINEAR)
binary_warped=cv2.warpPerspective(imgThr, M_persp, img_size, flags=cv2.INTER_LINEAR)
plt.figure(figsize=(20,15))
gs1=gridspec.GridSpec(2,2)
gs1.update(wspace=0.02, hspace=0.02)
ax1=plt.subplot(gs1[0])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.title('Image')
plt.imshow(imgRGB)
ax1=plt.subplot(gs1[1])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.title('Binary perspective')
plt.imshow(RGB_warped)
ax1=plt.subplot(gs1[2])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.title('Unperspective')
plt.imshow(img_unpersp)
ax1=plt.subplot(gs1[3])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.title('Histogram')
histogram=np.sum(binary_warped[binary_warped.shape[0]/2:,:], axis=0)
plt.plot(histogram)
Out[13]:
In [14]:
out_img=np.dstack((binary_warped, binary_warped, binary_warped))*255
mid=np.int(histogram.shape[0]/2)
left_base=np.argmax(histogram[:mid])
right_base=np.argmax(histogram[mid:])+mid
print("Left base: ",left_base)
print("Right base:",right_base)
plt.plot(histogram)
plt.plot([left_base,right_base], [0,0], 'ro')
Out[14]:
In [15]:
def fitlines(binary_warped):
histogram=np.sum(binary_warped[binary_warped.shape[0]/2:,:], axis=0)
out_img=np.dstack((binary_warped, binary_warped, binary_warped))*255
mid=np.int(histogram.shape[0]/2)
left_base=np.argmax(histogram[:mid])
right_base=np.argmax(histogram[mid:])+mid
nwindows=9
window_height=np.int(binary_warped.shape[0]/nwindows)
nonzero=binary_warped.nonzero()
nonzeroy=np.array(nonzero[0])
nonzerox=np.array(nonzero[1])
leftx_current=left_base
rightx_current=right_base
margin=100
minpix=50
left_lane_inds=[]
right_lane_inds=[]
for window in range(nwindows):
win_y_low=binary_warped.shape[0]-(window+1)*window_height
win_y_high=binary_warped.shape[0]-window*window_height
win_xleft_low=leftx_current-margin
win_xleft_high=leftx_current+margin
win_xright_low=rightx_current-margin
win_xright_high=rightx_current+margin
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(255,0,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(255,0,0), 2)
good_left_inds=((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) &
(nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds=((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) &
(nonzerox < win_xright_high)).nonzero()[0]
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
if len(good_left_inds) > minpix: leftx_current=np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix: rightx_current=np.int(np.mean(nonzerox[good_right_inds]))
left_lane_inds=np.concatenate(left_lane_inds)
right_lane_inds=np.concatenate(right_lane_inds)
leftx=nonzerox[left_lane_inds]
lefty=nonzeroy[left_lane_inds]
rightx=nonzerox[right_lane_inds]
righty=nonzeroy[right_lane_inds]
left_fit =[] if len(leftx) == 0 else np.polyfit(lefty, leftx, 2)
right_fit =[] if len(rightx) == 0 else np.polyfit(righty, rightx, 2)
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]]=[255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]]=[0, 0, 255]
return left_fit, right_fit,out_img
In [16]:
cal_images=glob.glob('test_images/test*.jpg')
global ref_left
global ref_right
global left_fit
global right_fit
gs1=gridspec.GridSpec(6,2)
plt.figure(figsize=(20,40))
gs1.update(wspace=0.02, hspace=0.02)
count=0
for i in cal_images:
img=cv2.imread(i)
imgRGB=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_undist=cv2.undistort(imgRGB, cam_mtx, cam_dist, None, cam_mtx)
imgThres_yellow=hls_color_thresh(img_undist,yellow_low,yellow_high)
imgThres_white=hls_color_thresh(img_undist,white_low,white_high)
imgThr_sobelx=sobel_x(img_undist,9,80,220)
img_mag_thr =np.zeros_like(imgThres_yellow)
img_mag_thr[(imgThres_yellow==1) | (imgThres_white==1) | (imgThr_sobelx==1)] =1
img_mag_thr[(imgThres_yellow==1) | (imgThres_white==1)] =1
img_size=(img_mag_thr.shape[1], img_mag_thr.shape[0])
binary_warped=cv2.warpPerspective(img_mag_thr, M_persp, img_size, flags=cv2.INTER_LINEAR)
left_fit, right_fit,out_img=fitlines(binary_warped)
ploty=np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx=left_fit[0]*ploty**2+left_fit[1]*ploty+left_fit[2]
right_fitx=right_fit[0]*ploty**2+right_fit[1]*ploty+right_fit[2]
ax1=plt.subplot(gs1[count])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.title(i)
plt.imshow(binary_warped, cmap='gray')
count+=1
ax1=plt.subplot(gs1[count])
plt.axis('on')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.imshow(out_img)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
binary_warped2=np.zeros((img_size[1], img_size[0],3))
binary_warped2[:,:,0]=binary_warped
binary_warped2[:,:,1]=binary_warped
binary_warped2[:,:,2]=binary_warped
plt.imshow(out_img)
result=cv2.addWeighted(binary_warped2, .8, out_img, .8, 0)
plt.imshow(result)
count+=1
In [17]:
def fit_continuous(left_fit, right_fit, binary_warped):
nonzero=binary_warped.nonzero()
nonzeroy=np.array(nonzero[0])
nonzerox=np.array(nonzero[1])
margin=100
left_lane_inds=((nonzerox > (left_fit[0]*(nonzeroy**2)+left_fit[1]*nonzeroy+left_fit[2]-margin)) &
(nonzerox < (left_fit[0]*(nonzeroy**2)+left_fit[1]*nonzeroy+left_fit[2]+margin)))
right_lane_inds=((nonzerox > (right_fit[0]*(nonzeroy**2)+right_fit[1]*nonzeroy+right_fit[2]-margin)) &
(nonzerox < (right_fit[0]*(nonzeroy**2)+right_fit[1]*nonzeroy+right_fit[2]+margin)))
leftx=nonzerox[left_lane_inds]
lefty=nonzeroy[left_lane_inds]
rightx=nonzerox[right_lane_inds]
righty=nonzeroy[right_lane_inds]
left_fit_updated =[] if len(leftx) == 0 else np.polyfit(lefty, leftx, 2)
right_fit_updated =[] if len(rightx) == 0 else np.polyfit(righty, rightx, 2)
return left_fit_updated, right_fit_updated
In [18]:
def curvature(left_fit, right_fit, binary_warped):
ploty=np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
y_eval=np.max(ploty)
ym_per_pix=30/img_size[0]
xm_per_pix=3.7/700
left_curverad=((1+(2*left_fit[0]*y_eval*ym_per_pix+left_fit[1])**2)**1.5) / np.absolute(2*left_fit[0])
right_curverad=((1+(2*right_fit[0]*y_eval*ym_per_pix+right_fit[1])**2)**1.5) / np.absolute(2*right_fit[0])
center=(((left_fit[0]*img_size[0]**2+left_fit[1]*img_size[0]+left_fit[2]) +(right_fit[0]*img_size[0]**2+right_fit[1]*img_size[0]+right_fit[2]) ) /2-640)*xm_per_pix
return left_curverad, right_curverad, center
In [19]:
def sanity_check(left_fit, right_fit, minSlope, maxSlope):
xm_per_pix=3.7/700
if len(left_fit) ==0 or len(right_fit) == 0:
status=False
d0=0
d1=0
else:
L_0=2*left_fit[0]*460+left_fit[1]
R_0=2*right_fit[0]*460+right_fit[1]
d0= np.abs(L_0-R_0)
L_1=2*left_fit[0]*img_size[0]+left_fit[1]
R_1=2*right_fit[0]*img_size[0]+right_fit[1]
d1= np.abs(L_1-R_1)
status=True if d0>= minSlope and d0<= maxSlope and d1>= minSlope and d1<= maxSlope else False
return(status, d0, d1)
In [20]:
def cool_visualization(center_data,graph,margin):
maxlen=55
if len(center_data)>maxlen:
center_data.pop(0)
pts=center_data
for i in range(1, len(center_data)):
if center_data[i-1] is None or center_data[i] is None:
continue
if i==len(center_data)-1:
thickness=4
elif i==len(center_data)-2:
thickness=3
else:
thickness=2
cv2.line(graph, (4*i-4,50-int(100*center_data[i-1])), (4*i,50-int(100*center_data[i])), (255, 0, 0), thickness)
graph=cv2.line(graph,(0,50),(margin,50),(255,255,0),2)
return graph
All together¶
In [26]:
global counter
global center_data
counter=0
ref_left =np.array([-0.0001,0,400])
ref_right=np.array([-0.0001,0,1000])
left_fit =np.array([-0.0001,0,400])
right_fit=np.array([-0.0001,0,1000])
center_data=[]
def process_image(image,yellow_low=np.array([0,100,100]),yellow_high=np.array([-0.0001,0,1000]),
white_low=np.array([-0.0001,0,400]),white_high=np.array([-0.0001,0,1000]) ,
sobel_kernel=9,min_thres=80, max_thres =220,min_slope=0,max_slope=.5):
img_undist=cv2.undistort(image, cam_mtx, cam_dist, None, cam_mtx)
global counter
global center_data
global ref_left
global ref_right
global left_fit
global right_fit
imgThres_yellow=hls_color_thresh(img_undist,yellow_low,yellow_high)
imgThres_white=hls_color_thresh(img_undist,white_low,white_high)
imgThr_sobelx=sobel_x(img_undist,sobel_kernel,min_thres ,max_thres) #Sobel x
img_mag_thr =np.zeros_like(imgThres_yellow)
img_mag_thr[(imgThres_yellow==1) | (imgThres_white==1) | (imgThr_sobelx==1)] =1
img_size=(img_mag_thr.shape[1], img_mag_thr.shape[0])
binary_warped=cv2.warpPerspective(img_mag_thr, M_persp, img_size, flags=cv2.INTER_LINEAR)
if counter==0:
left_fit, right_fit,out_imgfit=fitlines(binary_warped)
else:
left_fit, right_fit=fit_continuous(left_fit, right_fit, binary_warped)
status_sanity, d0, d1 =sanity_check(left_fit, right_fit, min_slope, max_slope)
if status_sanity == True:
ref_left,ref_right=left_fit,right_fit
counter+=1
else:
left_fit,right_fit=ref_left,ref_right
left_curv, right_curv, center_off=curvature(left_fit, right_fit, binary_warped)
color=(255,255, 255)
warp_zero=np.zeros_like(binary_warped).astype(np.uint8)
color_warp=np.dstack((warp_zero, warp_zero, warp_zero))
ploty=np.linspace(0, binary_warped.shape[0]-1,binary_warped.shape[0] )
left_fitx=left_fit[0]*ploty**2+left_fit[1]*ploty+left_fit[2]
right_fitx=right_fit[0]*ploty**2+right_fit[1]*ploty+right_fit[2]
pts_left=np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right=np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts=np.hstack((pts_left, pts_right))
cv2.fillPoly(color_warp, np.int_([pts]), color)
newwarp=cv2.warpPerspective(color_warp, Minv_persp, (color_warp.shape[1], color_warp.shape[0]))
result=cv2.addWeighted(img_undist, 1, newwarp, 0.3, 0)
img_merge= result
img_birds=color_warp
margin=300
top_margin=50
h=300
img_out=np.zeros((img_size[1],img_size[0],3), dtype=np.uint8)
graph=np.zeros((100,margin,3),dtype=np.uint8)
center_data.append(center_off)
graph=cool_visualization(center_data,graph,margin)
img_out[img_size[1]-h-100:img_size[1]-h,img_size[0]-margin:img_size[0], :] =graph
img_out[top_margin:img_size[1],0:img_size[0]-margin,0:3] =cv2.resize(img_merge,(img_size[0]-margin,img_size[1]-top_margin))
img_out[img_size[1]-h:img_size[1],img_size[0]-margin:img_size[0], 0] =cv2.resize(img_mag_thr*255,(margin,h))
img_out[img_size[1]-h:img_size[1],img_size[0]-margin:img_size[0], 1] =cv2.resize(img_mag_thr*255,(margin,h))
img_out[img_size[1]-h:img_size[1],img_size[0]-margin:img_size[0], 2] =cv2.resize(img_mag_thr*255,(margin,h))
TextL="-Left curvature: "+str(int(left_curv))+" m"
TextR="-Right curvature: "+str(int(right_curv))+ " m"
TextC="-Center offset: "+str(round( center_off,2))+"m"
TextUp="-Updating: "+str(status_sanity)
TextCent= "-Center_data size: "+str(len(center_data))
fontScale=.5
thickness=1
fontFace=cv2.FONT_HERSHEY_TRIPLEX
cv2.putText(img_out, "DATA", (img_size[0]-margin+100,60), fontFace, .8,(255,255,255), thickness, lineType=cv2.LINE_AA)
cv2.putText(img_out, TextL, (img_size[0]-margin+20,100), fontFace, fontScale,(255,255,255), thickness, lineType=cv2.LINE_AA)
cv2.putText(img_out, TextR, (img_size[0]-margin+20,130), fontFace, fontScale,(255,255,255), thickness, lineType=cv2.LINE_AA)
cv2.putText(img_out, TextC, (img_size[0]-margin+20,160), fontFace, fontScale,(255,255,255), thickness, lineType=cv2.LINE_AA)
cv2.putText(img_out, TextUp, (img_size[0]-margin+20,190), fontFace, fontScale,(255,255,255), thickness, lineType=cv2.LINE_AA)
cv2.putText(img_out, "Center offset history", (img_size[0]-margin+70,300), fontFace, .4,(255,255,255), thickness, lineType=cv2.LINE_AA)
cv2.putText(img_out, "Lucas Gago SDCND P4 v0.8", (5,30), fontFace, .7,(255,255,255), thickness, lineType=cv2.LINE_AA)
return img_out
Results¶
In [27]:
img=cv2.imread("test_images/test2.jpg")
imgRGB=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img2= process_image(imgRGB)
plt.figure(figsize=(30,20))
plt.imshow(img2)
Out[27]:
In [ ]:
#Create video file pipeline
output='out_project_video.mp4'
clip1=VideoFileClip("project_video.mp4")
out_clip=clip1.fl_image(process_image)
%time out_clip.write_videofile(output, audio=False)
print(counter)
In [28]:
output='out_project_video.mp4'
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(output))